Heart rate monitoring using hydraulic bed sensor ballistocardiogram1

Rosales, Licet; Su, Bo; Skubic, Marjorie; Ho, K. C.

doi:10.3233/ais-170423

Cited by 33 publications

(20 citation statements)

References 26 publications

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“…Rosales et al [67] applied the clustering-based approach [62] and the Hilbert transform approach [63] to ballistocardiogram signal collected from four male senior residents. The signals were collected from residents over a two to four months period under in-home living conditions.…”

Section: Hydraulic-based Sensorsmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

126

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There are several algorithms for analyzing and interpreting cardiorespiratory signals obtained from in-bed based sensors. In sum, these algorithms can be broadly grouped into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of these algorithms is given below to highlight which category of algorithms will be used in our analysis. First, time-domain algorithms are mainly focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of the nonlinear and nonstationary behavior of the ballistocardiogram signal. The implication is that the ballistocardiogram signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected. In other words, the selected component contains only information about the heart cycles or respiratory cycles, respectively. Interbeat intervals can be found easily by applying a simple peak detector. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property. Furthermore, the training step should be repeated whenever the data collection protocol has been changed.

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Section: Hydraulic-based Sensorsmentioning

confidence: 99%

Ballistocardiogram signal processing: a review

Sadek

Biswas

Abdulrazak

2019

Health Inf Sci Syst

126

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“…The long-term evolution of the uses of the person is important and relevant to the improvement or degradation of living conditions of the person. Other data can be acquired, such as heart rate, to be able to monitor in long terms the evolution of this data, and possibly in the future to raise alarms such as described before in the distress situations detection [78].…”

Section: Prevention/assistance To Healthcarementioning

confidence: 99%

Internet of Things for enabling smart environments: A technology-centric perspective

Gómez

Chessa

Fleury

et al. 2019

AIS

115

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The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

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“…The hydraulic bed sensor has been developed at the Center for Eldercare and Rehabilitation Technology (CERT) at the University of Missouri. A BCG device provides a noninvasive, low-cost, and robust solution for capturing physiological parameters such as heart rate and respiration rate, during sleep [6,10,11]. The BCG sensor has four transducers.…”

Section: Sensors and Datasetsmentioning

confidence: 99%

Non-invasive Classification of Sleep Stages with a Hydraulic Bed Sensor Using Deep Learning

Gargees

Keller

Popescu

et al. 2019

How AI Impacts Urban Living and Public Health

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The quality of sleep has a significant impact on health and life. This study adopts the structure of hierarchical classification to develop an automatic sleep stage classification system using ballistocardiogram (BCG) signals. A leave-one-subject-out cross validation (LOSO-CS) procedure is used for testing classification performance. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Deep Neural Networks DNNs are complementary in their modeling capabilities; while CNNs have the advantage of reducing frequency variations, LSTMs are good at temporal modeling. A transfer learning (TL) technique is used to pre-train our CNN model on posture data and then fine-tune it on the sleep stage data. We used a ballistocardiography (BCG) bed sensor to collect both posture and sleep stage data to provide a non-invasive, in-home monitoring system that tracks changes in health of the subjects over time. Polysomnography (PSG) data from a sleep lab was used as the ground truth for sleep stages, with the emphasis on three sleep stages, specifically, awake, rapid eye movement (REM) and non-REM sleep (NREM). Our results show an accuracy of 95.3%, 84% and 93.1% for awake, REM and NREM respectively on a group of patients from the sleep lab.

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Heart rate monitoring using hydraulic bed sensor ballistocardiogram1

Cited by 33 publications

References 26 publications

Ballistocardiogram signal processing: a review

Ballistocardiogram signal processing: a review

Internet of Things for enabling smart environments: A technology-centric perspective

Non-invasive Classification of Sleep Stages with a Hydraulic Bed Sensor Using Deep Learning

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